Detection of clustered anomalies in single-voxel morphometry as a rapid automated method for identifying intracranial aneurysms

Allenby, Mark C.; Liang, Ee Shern; Harvey, James; Woodruff, Maria A.; Prior, Marita; Winter, Chris; Alonso‐Caneiro, David

doi:10.1016/j.compmedimag.2021.101888

Cited by 7 publications

(2 citation statements)

References 36 publications

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“…The method triangulates the surface of the vascular structures and detects aneurysms with a translation, rotation, and scale-invariant classifier. Allenby et al (2021) propose a solution by characterizing single-voxel morphometry of segmented neurovasculature. Next, correlations between voxel-specific morphometries are estimated, and spatially-clustered outliers are classified as intracranial aneurysms.…”

Section: Task 1: Aneurysm Detectionmentioning

confidence: 99%

Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge

Ivantsits

Goubergrits

Kuhnigk

et al. 2022

Medical Image Analysis

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Section: Task 1: Aneurysm Detectionmentioning

confidence: 99%

Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge

Ivantsits

Goubergrits

Kuhnigk

et al. 2022

Medical Image Analysis

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“…Figure 1 shows that overall, 1736 studies met the search criteria and 99 potentially eligible full-text articles were assessed. Forty-three studies ranging from October 2004 to August 2021 were included 21–63. The total number of patient cases used for both training and testing was 18 143, and of these 10 625 patients had aneurysms, with a combined total of 12 990 aneurysms (datasets that were used across different studies were only included once).…”

Section: Resultsmentioning

confidence: 99%

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis

Din

Agarwal

Grzeda

et al. 2022

J NeuroIntervent Surg

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BackgroundSubarachnoid hemorrhage from cerebral aneurysm rupture is a major cause of morbidity and mortality. Early aneurysm identification, aided by automated systems, may improve patient outcomes. Therefore, a systematic review and meta-analysis of the diagnostic accuracy of artificial intelligence (AI) algorithms in detecting cerebral aneurysms using CT, MRI or DSA was performed.MethodsMEDLINE, Embase, Cochrane Library and Web of Science were searched until August 2021. Eligibility criteria included studies using fully automated algorithms to detect cerebral aneurysms using MRI, CT or DSA. Following Preferred Reporting Items for Systematic Reviews and Meta-Analysis: Diagnostic Test Accuracy (PRISMA-DTA), articles were assessed using Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Meta-analysis included a bivariate random-effect model to determine pooled sensitivity, specificity, and area under the receiver operator characteristic curve (ROC-AUC). PROSPERO: CRD42021278454.Results43 studies were included, and 41/43 (95%) were retrospective. 34/43 (79%) used AI as a standalone tool, while 9/43 (21%) used AI assisting a reader. 23/43 (53%) used deep learning. Most studies had high bias risk and applicability concerns, limiting conclusions. Six studies in the standalone AI meta-analysis gave (pooled) 91.2% (95% CI 82.2% to 95.8%) sensitivity; 16.5% (95% CI 9.4% to 27.1%) false-positive rate (1-specificity); 0.936 ROC-AUC. Five reader-assistive AI studies gave (pooled) 90.3% (95% CI 88.0% – 92.2%) sensitivity; 7.9% (95% CI 3.5% to 16.8%) false-positive rate; 0.910 ROC-AUC.ConclusionAI has the potential to support clinicians in detecting cerebral aneurysms. Interpretation is limited due to high risk of bias and poor generalizability. Multicenter, prospective studies are required to assess AI in clinical practice.

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Image analyses for engineering advanced tissue biomanufacturing processes

Allenby

Woodruff

2022

Biomaterials

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Detection of clustered anomalies in single-voxel morphometry as a rapid automated method for identifying intracranial aneurysms

Cited by 7 publications

References 36 publications

Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge

Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge

Detection of cerebral aneurysms using artificial intelligence: a systematic review and meta-analysis

Image analyses for engineering advanced tissue biomanufacturing processes

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